• Data prep
  • Phylum Level Microbiome and Food
    • Correlation between Phylum and HEI scores
      • Start of Study

Last updated: 2020-02-27

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Knit directory: Fiber_Intervention_Study/

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Data prep

This is only to recode some variables to ease interpretation.

microbiome_data$meta.dat <- microbiome_data$meta.dat %>%
  mutate(intB = ifelse(Intervention=="B", 1,0),
         time = as.numeric(Week) - 1,
         female = ifelse(Gender == "F", 1, 0),
         hispanic = ifelse(Ethnicity %in% c("White", "Asian", "Native America"), 1, 0))

Phylum Level Microbiome and Food

Get the phylum level data

data.phylum <-get_combined_data(mydata=microbiome_data,
                                taxa.level="Phylum")

#phylum 
bugs <- data.phylum$bug.list
bugs
 [1] "Actinobacteria"  "Bacteroidetes"   "Cyanobacteria"   "Euryarchaeota"  
 [5] "Firmicutes"      "Fusobacteria"    "Lentisphaerae"   "Proteobacteria" 
 [9] "Tenericutes"     "Verrucomicrobia"

Correlation between Phylum and HEI scores

Start of Study

Actinobacteria

subdat <- filter(data.phylum$full_data,
                 Week == "1" & Bug == bugs[1])

ggplot(subdat, aes(x=Abundance, y=HEI_Total,
                   color=Intervention))+
  geom_point(size=3) +
  labs(title = paste0("Phylum: ", bugs[1]))
Warning: Removed 2 rows containing missing values (geom_point).

Bacteroidetes

subdat <- filter(data.phylum$full_data,
                 Week == "1" & Bug == bugs[2])

ggplot(subdat, aes(x=Abundance, y=HEI_Total,
                   color=Intervention))+
  geom_point(size=3) +
  labs(title = paste0("Phylum: ", bugs[2]))
Warning: Removed 2 rows containing missing values (geom_point).

Cyanobacteria

subdat <- filter(data.phylum$full_data,
                 Week == "1" & Bug == bugs[3])

ggplot(subdat, aes(x=Abundance, y=HEI_Total,
                   color=Intervention))+
  geom_point(size=3) +
  labs(title = paste0("Phylum: ", bugs[3]))
Warning: Removed 2 rows containing missing values (geom_point).

Euryarchaeota

subdat <- filter(data.phylum$full_data,
                 Week == "1" & Bug == bugs[4])

ggplot(subdat, aes(x=Abundance, y=HEI_Total,
                   color=Intervention))+
  geom_point(size=3) +
  labs(title = paste0("Phylum: ", bugs[4]))
Warning: Removed 2 rows containing missing values (geom_point).

Firmicutes

subdat <- filter(data.phylum$full_data,
                 Week == "1" & Bug == bugs[5])

ggplot(subdat, aes(x=Abundance, y=HEI_Total,
                   color=Intervention))+
  geom_point(size=3) +
  labs(title = paste0("Phylum: ", bugs[5]))
Warning: Removed 2 rows containing missing values (geom_point).

Fusobacteria

subdat <- filter(data.phylum$full_data,
                 Week == "1" & Bug == bugs[6])

ggplot(subdat, aes(x=Abundance, y=HEI_Total,
                   color=Intervention))+
  geom_point(size=3) +
  labs(title = paste0("Phylum: ", bugs[6]))
Warning: Removed 2 rows containing missing values (geom_point).

Lentisphaerae

subdat <- filter(data.phylum$full_data,
                 Week == "1" & Bug == bugs[7])

ggplot(subdat, aes(x=Abundance, y=HEI_Total,
                   color=Intervention))+
  geom_point(size=3) +
  labs(title = paste0("Phylum: ", bugs[7]))
Warning: Removed 2 rows containing missing values (geom_point).

Proteobacteria

subdat <- filter(data.phylum$full_data,
                 Week == "1" & Bug == bugs[8])

ggplot(subdat, aes(x=Abundance, y=HEI_Total,
                   color=Intervention))+
  geom_point(size=3) +
  labs(title = paste0("Phylum: ", bugs[8]))
Warning: Removed 2 rows containing missing values (geom_point).

Tenericutes

subdat <- filter(data.phylum$full_data,
                 Week == "1" & Bug == bugs[9])

ggplot(subdat, aes(x=Abundance, y=HEI_Total,
                   color=Intervention))+
  geom_point(size=3) +
  labs(title = paste0("Phylum: ", bugs[9]))
Warning: Removed 2 rows containing missing values (geom_point).

Verrucomicrobia

subdat <- filter(data.phylum$full_data,
                 Week == "1" & Bug == bugs[10])

ggplot(subdat, aes(x=Abundance, y=HEI_Total,
                   color=Intervention))+
  geom_point(size=3) +
  labs(title = paste0("Phylum: ", bugs[10]))
Warning: Removed 2 rows containing missing values (geom_point).


sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] viridis_0.5.1     viridisLite_0.3.0 gridExtra_2.3     xtable_1.8-4     
 [5] kableExtra_1.1.0  plyr_1.8.4        data.table_1.12.6 readxl_1.3.1     
 [9] forcats_0.4.0     stringr_1.4.0     dplyr_0.8.3       purrr_0.3.3      
[13] readr_1.3.1       tidyr_1.0.0       tibble_2.1.3      ggplot2_3.2.1    
[17] tidyverse_1.3.0   lmerTest_3.1-1    lme4_1.1-21       Matrix_1.2-17    
[21] phyloseq_1.30.0  

loaded via a namespace (and not attached):
 [1] nlme_3.1-140        fs_1.3.1            lubridate_1.7.4    
 [4] RColorBrewer_1.1-2  webshot_0.5.2       httr_1.4.1         
 [7] rprojroot_1.3-2     numDeriv_2016.8-1.1 tools_3.6.1        
[10] backports_1.1.5     R6_2.4.1            vegan_2.5-6        
[13] DBI_1.0.0           lazyeval_0.2.2      BiocGenerics_0.32.0
[16] mgcv_1.8-28         colorspace_1.4-1    permute_0.9-5      
[19] ade4_1.7-13         withr_2.1.2         tidyselect_0.2.5   
[22] compiler_3.6.1      git2r_0.26.1        cli_1.1.0          
[25] rvest_0.3.5         Biobase_2.46.0      xml2_1.2.2         
[28] labeling_0.3        scales_1.1.0        digest_0.6.23      
[31] minqa_1.2.4         rmarkdown_1.18      XVector_0.26.0     
[34] pkgconfig_2.0.3     htmltools_0.4.0     dbplyr_1.4.2       
[37] rlang_0.4.2         rstudioapi_0.10     farver_2.0.1       
[40] generics_0.0.2      jsonlite_1.6        magrittr_1.5       
[43] biomformat_1.14.0   Rcpp_1.0.3          munsell_0.5.0      
[46] S4Vectors_0.24.1    Rhdf5lib_1.8.0      ape_5.3            
[49] lifecycle_0.1.0     stringi_1.4.3       yaml_2.2.0         
[52] MASS_7.3-51.4       zlibbioc_1.32.0     rhdf5_2.30.1       
[55] grid_3.6.1          parallel_3.6.1      promises_1.1.0     
[58] crayon_1.3.4        lattice_0.20-38     Biostrings_2.54.0  
[61] haven_2.2.0         splines_3.6.1       multtest_2.42.0    
[64] hms_0.5.2           zeallot_0.1.0       knitr_1.26         
[67] pillar_1.4.2        igraph_1.2.4.2      boot_1.3-22        
[70] reshape2_1.4.3      codetools_0.2-16    stats4_3.6.1       
[73] reprex_0.3.0        glue_1.3.1          evaluate_0.14      
[76] modelr_0.1.5        vctrs_0.2.0         nloptr_1.2.1       
[79] httpuv_1.5.2        foreach_1.4.7       cellranger_1.1.0   
[82] gtable_0.3.0        assertthat_0.2.1    xfun_0.11          
[85] broom_0.5.2         later_1.0.0         survival_2.44-1.1  
[88] iterators_1.0.12    IRanges_2.20.1      workflowr_1.5.0    
[91] cluster_2.1.0